{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"An example program that uses the elsapy module\"\"\"\n",
    "\n",
    "from elsapy.elsclient import ElsClient\n",
    "from elsapy.elsprofile import ElsAuthor, ElsAffil\n",
    "from elsapy.elsdoc import FullDoc, AbsDoc\n",
    "from elsapy.elssearch import ElsSearch\n",
    "import json\n",
    "    \n",
    "## Load configuration\n",
    "con_file = open(\"config.json\")\n",
    "config = json.load(con_file)\n",
    "con_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Initialize client\n",
    "client = ElsClient(config['apikey'])\n",
    "#client.inst_token = config['insttoken']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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'10.1111/jch.13700', '10.5213/inj.19381534.077', '10.1016/j.ygeno.2018.01.003', 'PMID:32002978', '10.1063/1.5120106', '10.1186/s12885-019-6101-7', '10.1016/j.ygeno.2018.05.016', '10.1016/j.jpedsurg.2018.10.071', '10.1371/journal.pone.0221460', '10.1093/bioinformatics/btz330', 'PMID:30919611', '10.1371/journal.pone.0207191', '10.1093/nar/gkz410', '10.3390/s19040833', '10.1021/acs.est.8b06586', '10.1103/PhysRevE.100.012201', '10.1097/MOG.0000000000000589', '\\n10.1186/s12911-019-0856-2', '10.3390/cancers11020212', '10.1186/s40708-019-0096-3', '10.1073/pnas.1900949116', '10.1038/s41586-019-0938-4', '10.1016/j.parkreldis.2019.02.003', '10.1016/j.humpath.2019.06.006', '10.1038/s41598-019-47565-y', '10.1177/1932296819839996', '10.3389/fpsyg.2019.02596', '10.1038/s41598-019-48016-4', '10.1186/s13244-019-0773-z', '10.12688/gatesopenres.12999.1', '10.1039/c9an00964g', '10.1007/s10071-019-01277-y', '10.1016/j.ultsonch.2019.104646', '10.1007/s00330-019-06558-1', '10.1016/j.apradiso.2019.108856', '10.1177/0142064X19858164', '\\n 10.3389/fgene.2019.01077', '10.3390/brainsci9080194', '10.3389/fcell.2019.00349', '10.1109/JBHI.2019.2919916', '10.1038/s41380-019-0365-9', '10.1021/acscombsci.8b00189', '10.3390/s19040753', '10.1007/s11948-019-00084-5', '10.1038/s41598-019-54961-x', '10.1103/PhysRevLett.123.213902', '10.1093/bioinformatics/bty511', '10.1177/0962280218805780', '10.1371/journal.pcbi.1007342', '10.1093/bioinformatics/bty1035', '10.1007/s10620-019-05791-4', '10.1111/den.13481', '10.1016/j.medengphy.2019.08.005', '10.1371/journal.pcbi.1006580', '10.3390/s19010125', '10.1021/acs.jpclett.9b02232', '10.1177/1559325819876218', '10.3390/jcm8101618', '10.12701/yujm.2019.00206', '10.1103/PhysRevLett.123.260501', '10.1186/s12911-019-0980-z', '10.1109/TNB.2019.2919188', '10.32687/0869-866X-2019-27-si1-630-636', '10.3389/fgene.2019.01182', '10.1098/rsos.190868', '10.1001/amajethics.2019.160', '10.26355/eurrev_201912_19686', '10.1093/nar/gkz328', '10.1109/JBHI.2019.2923773', '10.3389/fnhum.2019.00170', '10.1259/bjr.20190610', '10.3390/s19112525', '10.1007/s00221-018-5441-x', '10.1016/j.neuroimage.2019.116333', '10.2196/13111', '10.1371/journal.pone.0208308', '10.1002/wcs.1488', 'PMID:30778510', '10.1038/s41598-019-41663-7', '10.1016/j.biopha.2019.109274', '10.3390/ijms20184462', '10.1371/journal.pone.0218946', '10.1007/s00018-018-2970-1', '10.1016/j.gene.2019.144150', '10.1007/s11033-018-4533-9', '10.1162/artl_a_00288', '10.3390/ijms20246108', '10.3390/ijms20020296', '10.1371/journal.pone.0215901', '10.1055/s-0038-1668122', '10.1016/j.ijrobp.2019.07.011', '10.1186/s12864-018-5400-8', '10.1007/s10142-018-0629-5', '10.1007/s10142-018-0635-7', '10.3892/mmr.2018.9641', '10.1016/j.ymeth.2018.11.015', '10.3892/mmr.2019.10308', '10.1016/j.jcmgh.2018.09.008', '10.1038/s41440-018-0177-3', '10.1055/a-0740-8631', '10.1007/s00259-019-04437-x', '10.1016/j.micpath.2019.04.033', '10.1016/j.scitotenv.2019.134230', '10.12659/MSM.917399', '10.1016/j.radonc.2019.11.023', '10.3390/s19214788', '10.1016/j.ophtha.2019.12.004', '10.3390/s19030613', '10.1016/j.ijmedinf.2019.104068', '10.1007/s00330-019-06371-w', '10.1016/j.jbi.2019.103364', '10.1080/09513590.2019.1650345', '10.1016/j.media.2019.101625', '10.1038/s41598-019-44852-6', '10.1037/xge0000630', '10.1097/CCO.0000000000000563', '10.1007/978-3-030-16391-4_8', '10.3389/fbioe.2019.00330', '10.3390/ma12182864', '10.1016/j.neuroimage.2019.06.053', '10.12659/MSM.917299', '10.1007/s11255-018-2044-1', '10.2214/AJR.18.20443', '10.1111/nph.15692', '10.1371/journal.pone.0214857', '10.3390/ijms20061309', '10.12659/MSM.917375', '10.1186/s12967-018-1761-7', '10.1371/journal.pone.0223173', '10.3390/ijms20174231', '10.1038/s41467-019-09278-8', '10.3390/s19071740', '10.1186/s40644-019-0203-y', '\\n 10.3390/s19030542', '10.1073/pnas.1820458116', '10.3389/fnbot.2019.00018', '10.1016/j.ijlp.2019.101479', '10.1016/j.dental.2019.10.001', '10.1007/s11684-019-0726-4', '10.1109/TBCAS.2019.2955641', '10.1007/s10916-019-1469-0', '10.1109/EMBC.2019.8857448', '10.1007/s00122-019-03493-z', '10.15252/embr.201949177', '10.1063/1.5090319', '10.1089/omi.2019.0020', '10.3389/fnbot.2019.00078', '10.5946/ce.2019.012', '10.1109/JBHI.2019.2914690', '10.1097/PRS.0000000000005673', '10.1093/jbcr/irz103', '10.1103/PhysRevE.99.043306', '10.1038/s41598-019-46074-2', '\\n10.1093/jnen/nlz122', '10.1148/radiol.2020200905', '10.1016/j.mri.2019.09.006', '10.1007/s12149-020-01460-z', '10.1007/s12350-018-1317-5', '10.3322/caac.21608', '10.1192/bjp.2019.127', '10.1002/humu.23948', '10.1002/adma.201906493', '10.3390/ijerph17031093', '10.2196/15510', '10.1038/s41598-020-62676-7', '10.1098/rsta.2019.0163', '10.1002/hbm.24968', '10.1136/gutjnl-2019-318860', '10.1007/s11571-019-09560-x', '10.1016/j.ijporl.2019.109833', '10.1016/j.msard.2020.102034', '10.1001/jamanetworkopen.2020.0255', '10.1212/WNL.0000000000009068', '10.3390/v12030268', '10.1089/omi.2019.0220', '10.1016/j.annonc.2020.04.003', 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'10.1038/s41586-020-2284-y', '10.1016/j.brainres.2020.146693', '10.1186/s12864-020-6542-z', '10.1089/ast.2019.2129', '10.2196/15411', '10.1016/j.drugalcdep.2019.107716', '10.3390/s20092489', '10.1002/ece3.6147', '10.1016/j.cmpb.2019.105162', '10.1111/den.13688', '10.3390/s20010293', '10.1097/MLR.0000000000001221', '10.1111/nyas.14320', 'PMID:32355515', '10.1002/jum.15071', '10.1158/2326-6066.CIR-19-0521', '10.4070/kcj.2019.0105', '10.1186/s12916-020-01563-4', '10.1016/j.cmpb.2020.105475', '10.2196/17279', '10.1002/1878-0261.12635', '10.3390/s20092460', '10.1016/j.canrad.2020.01.011', '10.1016/j.actbio.2020.02.007', '10.1016/j.brainres.2020.146700', '\\n 10.1021/acsami.9b22369', '10.1186/s12969-020-0404-8', '10.1002/jemt.23429', '10.1016/j.cyto.2020.155051', '10.1016/j.artmed.2019.101782', '10.1016/j.enfcli.2019.09.024', '10.21037/atm.2019.12.150', '10.1089/brain.2019.0722', '10.1177/0886260520913648', '10.1093/jamia/ocz166', '10.1007/s00345-020-03214-y', '10.3390/s20092467', '10.1111/febs.15314', '10.3390/diagnostics10040196', '10.1002/mp.13978', '10.3390/bs10020055', '10.3389/fpsyt.2020.00026', '10.1007/s11548-019-02114-w', '10.3390/s20041068', '10.3389/fnhum.2019.00457', '']\n"
     ]
    }
   ],
   "source": [
    "data = open(\"/Users/linar/OneDrive/Документы/GitHub/AI-Team-Projetc/labeled_set_new(2).txt\", \"r+\")\n",
    "dois = []\n",
    "data = data.read()\n",
    "#print (data)\n",
    "data = data.split(\"-------------------------------------------\")\n",
    "for d in data:\n",
    "    d = d.split(\"  |  \")\n",
    "    dois.append(d[0])\n",
    "dois_new = []\n",
    "for doi in dois:\n",
    "    doi = doi.replace(\" \\n\", \"\")\n",
    "    \n",
    "    dois_new.append(doi)\n",
    "print(dois_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "doi_doc.title:  Using neural networks to determine the contribution of danshensu to its multiple cardiovascular activities in acute myocardial infarction rats \n",
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      "doi_doc.title:  Using a symbolic process model as input for model-based fMRI analysis: Locating the neural correlates of problem state replacements \n",
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      "doi_doc.title:  Egg production forecasting: Determining efficient modeling approaches\n",
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      "doi_doc.title:  Effects of dioxin exposure in Eisenia andrei: integration of biomarker data by an Expert System to rank the development of pollutant-induced stress syndrome in earthworms \n",
      "doi_doc.title:  Safety Profile of Robot-Assisted Radical Prostatectomy: A Standardized Report of Complications in 3317 Patients\n",
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      "doi_doc.title:  Search for patterns of functional specificity in the brain: A nonparametric hierarchical Bayesian model for group fMRI data \n",
      "doi_doc.title:  Parsing recursive sentences with a connectionist model including a neural stack and synaptic gating \n",
      "doi_doc.title:  Behavioural robustness: A link between distributed mechanisms and coupled transient dynamics \n",
      "doi_doc.title:  Identification of MCI individuals using structural and functional connectivity networks \n",
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      "doi_doc.title:  Detecting disease genes based on semi-supervised learning and protein–protein interaction networks \n",
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      "doi_doc.title:  How much benefit does Intelligent Speed Adaptation deliver: An analysis of its potential contribution to safety and environment \n",
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      "doi_doc.title:  Semi-supervised cluster analysis of imaging data \n",
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      "doi_doc.title:  A framework for diagnosis of urinary incontinence disease based on scoring measures and automatic classifiers \n",
      "doi_doc.title:  Simple methods for segmentation and measurement of diabetic retinopathy lesions in retinal fundus images \n",
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      "doi_doc.title:  Probabilistic gait classification in children with cerebral palsy: A Bayesian approach \n",
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      "doi_doc.title:  AskHERMES: An online question answering system for complex clinical questions\n",
      "doi_doc.title:  MEDRank: Using graph-based concept ranking to index biomedical texts \n",
      "doi_doc.title:  Prediction of regulatory interactions in Arabidopsis using gene-expression data and support vector machines \n",
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      "doi_doc.title:  Unsupervised multiparametric classification of dynamic susceptibility contrast imaging: Study of the healthy brain \n",
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      "doi_doc.title:  Manifold learning for image-based breathing gating in ultrasound and MRI \n",
      "doi_doc.title:  Pay as You Speed, ISA with incentives for not speeding: A case of test driver recruitment \n",
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      "doi_doc.title:  Diffusion tensor fiber tractography of the olfactory tract \n",
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      "doi_doc.title:  Genome-wide association identifies diverse causes of common variable immunodeficiency \n",
      "doi_doc.title:  AFP-Pred: A random forest approach for predicting antifreeze proteins from sequence-derived properties \n",
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      "doi_doc.title:  Predicting human microRNA precursors based on an optimized feature subset generated by GA–SVM\n",
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      "doi_doc.title:  Unsupervised 4D myocardium segmentation with a Markov Random Field based deformable model \n",
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      "doi_doc.title:  Tuning of an optimal fuzzy PID controller with stochastic algorithms for networked control systems with random time delay \n",
      "doi_doc.title:  A novel detection method of non–small cell lung cancer using multiplexed bead-based serum biomarker profiling\n",
      "doi_doc.title:  Multivessel beating heart robotic myocardial revascularization increases morbidity and mortality\n",
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      "doi_doc.title:  Anatomic Renal Artery Branch Microdissection to Facilitate Zero-Ischemia Partial Nephrectomy \n",
      "doi_doc.title:  Perioperative Outcomes of Robot-Assisted Radical Prostatectomy Compared With Open Radical Prostatectomy: Results From the Nationwide Inpatient Sample\n",
      "                  \n",
      "               \n",
      "             \n",
      "doi_doc.title:  Long-term Biochemical Recurrence Rates After Robot-assisted Radical Prostatectomy: Analysis of a Single-center Series of Patients With a Minimum Follow-up of 5 Years \n",
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      "doi_doc.title:  Use of TILLING and robotised enzyme assays to generate an allelic series of Arabidopsis thaliana mutants with altered ADP-glucose pyrophosphorylase activity \n",
      "doi_doc.title:  Factors Determining Functional Outcomes After Radical Prostatectomy: Robot-Assisted Versus Retropubic \n",
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      "doi_doc.title:  GPU-accelerated Chemical Similarity Assessment for Large Scale Databases\n",
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      "doi_doc.title:  Évolution des concepts dans les cancers des voies aérodigestives supérieures, sous l’égide de l’Intergroupe ORL (GORTEC, GETTEC, GERCOR) \n",
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      "doi_doc.title:  Robot-assisted laparoscopic nephron sparing surgery for tumors over 4 cm: Operative results and preliminary oncologic outcomes from a multicentre French study \n",
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      "doi_doc.title:  Assessment of the implementation of the model of integrated and humanised midwifery health services in Santiago, Chile \n",
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      "doi_doc.title:  Development and validation of a CT-based radiomic nomogram for preoperative prediction of early recurrence in advanced gastric cancer \n",
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      "doi_doc.title:  Artificial Intelligence Classification of Central Visual Field Patterns in Glaucoma \n",
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      "doi_doc.title:  Comparison of supervised machine learning classification techniques in prediction of locoregional recurrences in early oral tongue cancer \n",
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      "doi_doc.title:  Unsupervised machine learning for the discovery of latent disease clusters and patient subgroups using electronic health records \n",
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      "doi_doc.title:  Multi-modal neuroimaging feature selection with consistent metric constraint for diagnosis of Alzheimer's disease \n",
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      "doi_doc.title:  Spectral signatures of serotonergic psychedelics and glutamatergic dissociatives \n",
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      "doi_doc.title:  The risks of risk. Regulating the use of machine learning for psychosis prediction \n",
      "doi_doc.title:  The Academy of Dental Materials: Providing roots and wings \n",
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      "doi_doc.title:  STrategically Acquired Gradient Echo (STAGE) imaging, part III: Technical advances and clinical applications of a rapid multi-contrast multi-parametric brain imaging method \n",
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      "doi_doc.title:  Panel 1: Biotechnology, biomedical engineering and new models of otitis media \n",
      "doi_doc.title:  Efficacy of robot-assisted gait training in multiple sclerosis: A systematic review and meta-analysis \n",
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      "doi_doc.title:  Deep learning radiomic nomogram can predict the number of lymph node metastasis in locally advanced gastric cancer: an international multicenter study\n",
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      "doi_doc.title:  Chapter Two Computational approaches for identifying potential inhibitors on targeting protein interactions in drug discovery \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "doi_doc.title:  IDRiD: Diabetic Retinopathy – Segmentation and Grading Challenge \n",
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      "doi_doc.title:  Texture analysis and multiple-instance learning for the classification of malignant lymphomas \n",
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      "doi_doc.title:  Self-calibrated brain network estimation and joint non-convex multi-task learning for identification of early Alzheimer's disease \n",
      "doi_doc.title:  Influence of Coronary Calcium on Diagnostic Performance of Machine Learning CT-FFR Results From MACHINE Registry\n",
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      "doi_doc.title:  Improved Accuracy in Optical Diagnosis of Colorectal Polyps Using Convolutional Neural Networks with Visual Explanations \n",
      "doi_doc.title:  Determinants of Cone and Rod Functions in Geographic Atrophy: AI-Based Structure-Function Correlation \n",
      "doi_doc.title:  Super-resolution reconstruction of knee magnetic resonance imaging based on deep learning \n",
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      "doi_doc.title:  Different sampling strategies for predicting landslide susceptibilities are deemed less consequential with deep learning \n",
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      "doi_doc.title:  Spatial Lifecourse Epidemiology Reporting Standards (ISLE-ReSt) statement\n",
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      "doi_doc.title:  English spoken word segmentation activates the prefrontal cortex and temporo-parietal junction in Chinese ESL learners: A functional near-infrared spectroscopy (fNIRS) study \n",
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      "doi_doc.title:  Subjective effects of combustible, vaporized, and edible cannabis: Results from a survey of adolescent cannabis users \n",
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      "doi_doc.title:  Deep learning, reusable and problem-based architectures for detection of consolidation on chest X-ray images \n",
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      "doi_doc.title:  Transfer learning using a multi-scale and multi-network ensemble for skin lesion classification \n",
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      "doi_doc.title:  Radiomics: A primer for the radiation oncologist \n",
      "doi_doc.title:  Decoding rejuvenating effects of mechanical loading on skeletal aging using in vivo μCT imaging and deep learning \n",
      "doi_doc.title:  Astrocyte signaling impacts the effects of human bone marrow mesenchymal stem cells secretome application into the hippocampus: A proliferation and morphometrical analysis on astrocytic cell populations \n",
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      "doi_doc.title:  Exploratory analysis of plasma cytokine/chemokine levels in 6-year-old children from a birth cohort study \n",
      "doi_doc.title:  A fusion framework to extract typical treatment patterns from electronic medical records \n",
      "doi_doc.title:  Potential legal issues when caring healthcare robot with communication in caring functions are used for older adult care \n",
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     ]
    },
    {
     "ename": "ValueError",
     "evalue": "No URI, ScienceDirect PII or DOI specified",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-6-4b21e5cb515f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0md\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdois_new\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m     \u001b[0mdoi_doc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mFullDoc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdoi\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0md\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mdoi_doc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mclient\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m         \u001b[0mprint\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m\"doi_doc.title: \"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdoi_doc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\elsapy\\elsdoc.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, uri, sd_pii, doi)\u001b[0m\n\u001b[0;32m     37\u001b[0m             \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__uri_base\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m'doi/'\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdoi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     38\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0muri\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mdoi\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 39\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'No URI, ScienceDirect PII or DOI specified'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     40\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     41\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Multiple identifiers specified; just need one.'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: No URI, ScienceDirect PII or DOI specified"
     ]
    }
   ],
   "source": [
    "## ScienceDirect (full-text) document example using DOI\n",
    "\n",
    "for d in dois_new:\n",
    "    \n",
    "    doi_doc = FullDoc(doi = d)\n",
    "    if doi_doc.read(client):\n",
    "        print (\"doi_doc.title: \", doi_doc.title)\n",
    "        doi_doc.write()   \n",
    "    else:\n",
    "        print (\"Read document failed.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "doc_srch has 25 results.\n"
     ]
    }
   ],
   "source": [
    "## Initialize doc search object using ScienceDirect and execute search, \n",
    "#   retrieving all results\n",
    "doc_srch = ElsSearch(\"artificial intelligence in medicine\",'sciencedirect')\n",
    "doc_srch.execute(client, get_all = False)\n",
    "print (\"doc_srch has\", len(doc_srch.results), \"results.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     S1076633219304581\n",
       "1     S1052515720300118\n",
       "2     S027263861930842X\n",
       "3     S2452318620300192\n",
       "4     S2352340919312491\n",
       "5     S0737080620300642\n",
       "6     S0889858819300966\n",
       "7     S0846537119300063\n",
       "8     S0303846720300615\n",
       "9     S0009912018311974\n",
       "10    S0960977620300096\n",
       "11    S2211883718301758\n",
       "12    S074906902030029X\n",
       "13    S2095809920300035\n",
       "14    S2589238X20300127\n",
       "15    S1001929419300288\n",
       "16    S0720048X19304243\n",
       "17    S2001037020300325\n",
       "18    S1473050218301721\n",
       "19    S0022073619307046\n",
       "20    S1076633219304428\n",
       "21    S235208171930193X\n",
       "22    S1532046419302308\n",
       "23    S0378378220301481\n",
       "24    S0303846720300755\n",
       "Name: pii, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "results = pd.DataFrame(doc_srch.results)\n",
    "results['pii']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pii_doc.title:  Artificial Intelligence in Medicine: Where Are We Now? \n",
      "pii_doc.title:  How Artificial Intelligence Will Impact Colonoscopy and Colorectal Screening \n",
      "pii_doc.title:  Artificial Intelligence in Nephrology: Core Concepts, Clinical Applications, and Perspectives \n",
      "pii_doc.title:  Machine learning and artificial intelligence in the service of medicine: Necessity or potentiality? \n",
      "pii_doc.title:  Clinical trial design data for electrocardiogram artificial intelligence-guided screening for low ejection fraction (EAGLE)\n",
      "pii_doc.title:  Using Artificial Intelligence to Predict Survivability Likelihood and Need for Surgery in Horses Presented With Acute Abdomen (Colic) \n",
      "pii_doc.title:  Artificial Intelligence in Radiation Oncology \n",
      "pii_doc.title:  Canadian Association of Radiologists White Paper on Ethical and Legal Issues Related to Artificial Intelligence in Radiology\n",
      "pii_doc.title:  Using artificial intelligence (AI) to predict postoperative surgical site infection: A retrospective cohort of 4046 posterior spinal fusions \n",
      "pii_doc.title:  Data science, artificial intelligence, and machine learning: Opportunities for laboratory medicine and the value of positive regulation \n",
      "pii_doc.title:  Using artificial intelligence to analyse and teach communication in healthcare\n",
      "pii_doc.title:  Artificial intelligence in medicine: What is it doing for us today? \n",
      "pii_doc.title:  Artificial Intelligence and Digital Tools: Future of Diabetes Care \n",
      "pii_doc.title:  From Brain Science to Artificial Intelligence\n",
      "pii_doc.title:  Artificial intelligence: The dawn of a new era for cutting-edge technology based diagnosis and treatment for stroke\n",
      "pii_doc.title:  Artificial Intelligence in Healthcare and Medicine: Promises, Ethical Challenges and Governance \n",
      "pii_doc.title:  Artificial intelligence applications for thoracic imaging \n",
      "pii_doc.title:  Artificial intelligence in tongue diagnosis: Using deep convolutional neural network for recognizing unhealthy tongue with tooth-mark\n",
      "pii_doc.title:  Artificial intelligence: A joint narrative on potential use in pediatric stem and immune cell therapies and regenerative medicine \n",
      "pii_doc.title:  Artificial intelligence for detecting mitral regurgitation using electrocardiography \n",
      "pii_doc.title:  Artificial Intelligence in Radiology: Summary of the AUR Academic Radiology and Industry Leaders Roundtable \n",
      "pii_doc.title:  Current status and applications of Artificial Intelligence (AI) in medical field: An overview \n",
      "pii_doc.title:  Transforming healthcare with big data analytics and artificial intelligence: A systematic mapping study \n",
      "pii_doc.title:  Artificial intelligence in medicine \n",
      "pii_doc.title:  Artificial intelligence outperforms human students in conducting neurosurgical audits \n"
     ]
    }
   ],
   "source": [
    "for pii in list(results['pii']):\n",
    "    pii_doc = FullDoc(sd_pii = pii)\n",
    "    if pii_doc.read(client):\n",
    "        print (\"pii_doc.title: \", pii_doc.title)\n",
    "        pii_doc.write()   \n",
    "    else:\n",
    "        print (\"Read document failed.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "doc_srch has 25 results.\n",
      "pii_doc.title:  A multi-context CNN ensemble for small lesion detection \n",
      "pii_doc.title:  The impact of machine learning on patient care: A systematic review \n",
      "pii_doc.title:  A novel method of motor imagery classification using eeg signal \n",
      "pii_doc.title:  The role of medical smartphone apps in clinical decision-support: A literature review \n",
      "pii_doc.title:  Combining clustering and classification ensembles: A novel pipeline to identify breast cancer profiles \n",
      "pii_doc.title:  Ophthalmic diagnosis using deep learning with fundus images – A critical review \n",
      "pii_doc.title:  A survey on computer-assisted Parkinson's Disease diagnosis \n",
      "pii_doc.title:  Pharmacological therapy selection of type 2 diabetes based on the SWARA and modified MULTIMOORA methods under a fuzzy environment \n",
      "pii_doc.title:  Using echo state networks for classification: A case study in Parkinson's disease diagnosis \n",
      "pii_doc.title:  Prediction of lung cancer incidence on the low-dose computed tomography arm of the National Lung Screening Trial: A dynamic Bayesian network \n",
      "pii_doc.title:  Modeling and processing up-to-dateness of patient information in probabilistic therapy decision support \n",
      "pii_doc.title:  Handling limited datasets with neural networks in medical applications: A small-data approach\n",
      "pii_doc.title:  An effective approach for CT lung segmentation using mask region-based convolutional neural networks \n",
      "pii_doc.title:  Multi-objective evolutionary design of antibiotic treatments \n",
      "pii_doc.title:  Toward Development of PreVoid Alerting System for Nocturnal Enuresis Patients: A Fuzzy-Based Approach for Determining the Level of Liquid Encased in Urinary Bladder \n",
      "pii_doc.title:  A multicenter random forest model for effective prognosis prediction in collaborative clinical research network \n",
      "pii_doc.title:  Predicting dementia with routine care EMR data \n",
      "pii_doc.title:  Fuzzy support vector machine-based personalizing method to address the inter-subject variance problem of physiological signals in a driver monitoring system \n",
      "pii_doc.title:  A recurrent neural network approach to predicting hemoglobin trajectories in patients with End-Stage Renal Disease \n",
      "pii_doc.title:  Learning from healthy and stable eyes: A new approach for detection of glaucomatous progression \n",
      "pii_doc.title:  A methodology based on multiple criteria decision analysis for combining antibiotics in empirical therapy \n",
      "pii_doc.title:  Classifying cancer pathology reports with hierarchical self-attention networks\n",
      "pii_doc.title:  Reinforcement learning application in diabetes blood glucose control: A systematic review \n",
      "pii_doc.title:  A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetes diagnosis \n",
      "pii_doc.title:  Artificial intelligence and the future of psychiatry: Insights from a global physician survey \n",
      "doc_srch has 25 results.\n",
      "pii_doc.title:  A multi-context CNN ensemble for small lesion detection \n",
      "pii_doc.title:  The impact of machine learning on patient care: A systematic review \n",
      "pii_doc.title:  A novel method of motor imagery classification using eeg signal \n",
      "pii_doc.title:  The role of medical smartphone apps in clinical decision-support: A literature review \n",
      "pii_doc.title:  Combining clustering and classification ensembles: A novel pipeline to identify breast cancer profiles \n",
      "pii_doc.title:  Ophthalmic diagnosis using deep learning with fundus images – A critical review \n",
      "pii_doc.title:  A survey on computer-assisted Parkinson's Disease diagnosis \n",
      "pii_doc.title:  Pharmacological therapy selection of type 2 diabetes based on the SWARA and modified MULTIMOORA methods under a fuzzy environment \n",
      "pii_doc.title:  Using echo state networks for classification: A case study in Parkinson's disease diagnosis \n",
      "pii_doc.title:  Prediction of lung cancer incidence on the low-dose computed tomography arm of the National Lung Screening Trial: A dynamic Bayesian network \n",
      "pii_doc.title:  Modeling and processing up-to-dateness of patient information in probabilistic therapy decision support \n",
      "pii_doc.title:  Handling limited datasets with neural networks in medical applications: A small-data approach\n",
      "pii_doc.title:  An effective approach for CT lung segmentation using mask region-based convolutional neural networks \n",
      "pii_doc.title:  Multi-objective evolutionary design of antibiotic treatments \n",
      "pii_doc.title:  Toward Development of PreVoid Alerting System for Nocturnal Enuresis Patients: A Fuzzy-Based Approach for Determining the Level of Liquid Encased in Urinary Bladder \n",
      "pii_doc.title:  A multicenter random forest model for effective prognosis prediction in collaborative clinical research network \n",
      "pii_doc.title:  Predicting dementia with routine care EMR data \n",
      "pii_doc.title:  Fuzzy support vector machine-based personalizing method to address the inter-subject variance problem of physiological signals in a driver monitoring system \n",
      "pii_doc.title:  A recurrent neural network approach to predicting hemoglobin trajectories in patients with End-Stage Renal Disease \n",
      "pii_doc.title:  Learning from healthy and stable eyes: A new approach for detection of glaucomatous progression \n",
      "pii_doc.title:  A methodology based on multiple criteria decision analysis for combining antibiotics in empirical therapy \n",
      "pii_doc.title:  Classifying cancer pathology reports with hierarchical self-attention networks\n",
      "pii_doc.title:  Reinforcement learning application in diabetes blood glucose control: A systematic review \n",
      "pii_doc.title:  A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetes diagnosis \n",
      "pii_doc.title:  Artificial intelligence and the future of psychiatry: Insights from a global physician survey \n",
      "doc_srch has 25 results.\n",
      "pii_doc.title:  A multi-context CNN ensemble for small lesion detection \n",
      "pii_doc.title:  The impact of machine learning on patient care: A systematic review \n",
      "pii_doc.title:  A novel method of motor imagery classification using eeg signal \n",
      "pii_doc.title:  The role of medical smartphone apps in clinical decision-support: A literature review \n",
      "pii_doc.title:  Combining clustering and classification ensembles: A novel pipeline to identify breast cancer profiles \n",
      "pii_doc.title:  Ophthalmic diagnosis using deep learning with fundus images – A critical review \n",
      "pii_doc.title:  A survey on computer-assisted Parkinson's Disease diagnosis \n",
      "pii_doc.title:  Pharmacological therapy selection of type 2 diabetes based on the SWARA and modified MULTIMOORA methods under a fuzzy environment \n",
      "pii_doc.title:  Using echo state networks for classification: A case study in Parkinson's disease diagnosis \n",
      "pii_doc.title:  Prediction of lung cancer incidence on the low-dose computed tomography arm of the National Lung Screening Trial: A dynamic Bayesian network \n",
      "pii_doc.title:  Modeling and processing up-to-dateness of patient information in probabilistic therapy decision support \n",
      "pii_doc.title:  Handling limited datasets with neural networks in medical applications: A small-data approach\n",
      "pii_doc.title:  An effective approach for CT lung segmentation using mask region-based convolutional neural networks \n",
      "pii_doc.title:  Multi-objective evolutionary design of antibiotic treatments \n",
      "pii_doc.title:  Toward Development of PreVoid Alerting System for Nocturnal Enuresis Patients: A Fuzzy-Based Approach for Determining the Level of Liquid Encased in Urinary Bladder \n",
      "pii_doc.title:  A multicenter random forest model for effective prognosis prediction in collaborative clinical research network \n",
      "pii_doc.title:  Predicting dementia with routine care EMR data \n",
      "pii_doc.title:  Fuzzy support vector machine-based personalizing method to address the inter-subject variance problem of physiological signals in a driver monitoring system \n",
      "pii_doc.title:  A recurrent neural network approach to predicting hemoglobin trajectories in patients with End-Stage Renal Disease \n",
      "pii_doc.title:  Learning from healthy and stable eyes: A new approach for detection of glaucomatous progression \n",
      "pii_doc.title:  A methodology based on multiple criteria decision analysis for combining antibiotics in empirical therapy \n",
      "pii_doc.title:  Classifying cancer pathology reports with hierarchical self-attention networks\n",
      "pii_doc.title:  Reinforcement learning application in diabetes blood glucose control: A systematic review \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pii_doc.title:  A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetes diagnosis \n",
      "pii_doc.title:  Artificial intelligence and the future of psychiatry: Insights from a global physician survey \n",
      "doc_srch has 25 results.\n",
      "pii_doc.title:  A multi-context CNN ensemble for small lesion detection \n",
      "pii_doc.title:  The impact of machine learning on patient care: A systematic review \n",
      "pii_doc.title:  A novel method of motor imagery classification using eeg signal \n",
      "pii_doc.title:  The role of medical smartphone apps in clinical decision-support: A literature review \n",
      "pii_doc.title:  Combining clustering and classification ensembles: A novel pipeline to identify breast cancer profiles \n",
      "pii_doc.title:  Ophthalmic diagnosis using deep learning with fundus images – A critical review \n",
      "pii_doc.title:  A survey on computer-assisted Parkinson's Disease diagnosis \n",
      "pii_doc.title:  Pharmacological therapy selection of type 2 diabetes based on the SWARA and modified MULTIMOORA methods under a fuzzy environment \n",
      "pii_doc.title:  Using echo state networks for classification: A case study in Parkinson's disease diagnosis \n",
      "pii_doc.title:  Prediction of lung cancer incidence on the low-dose computed tomography arm of the National Lung Screening Trial: A dynamic Bayesian network \n",
      "pii_doc.title:  Modeling and processing up-to-dateness of patient information in probabilistic therapy decision support \n",
      "pii_doc.title:  Handling limited datasets with neural networks in medical applications: A small-data approach\n",
      "pii_doc.title:  An effective approach for CT lung segmentation using mask region-based convolutional neural networks \n",
      "pii_doc.title:  Multi-objective evolutionary design of antibiotic treatments \n",
      "pii_doc.title:  Toward Development of PreVoid Alerting System for Nocturnal Enuresis Patients: A Fuzzy-Based Approach for Determining the Level of Liquid Encased in Urinary Bladder \n",
      "pii_doc.title:  A multicenter random forest model for effective prognosis prediction in collaborative clinical research network \n",
      "pii_doc.title:  Predicting dementia with routine care EMR data \n",
      "pii_doc.title:  Fuzzy support vector machine-based personalizing method to address the inter-subject variance problem of physiological signals in a driver monitoring system \n",
      "pii_doc.title:  A recurrent neural network approach to predicting hemoglobin trajectories in patients with End-Stage Renal Disease \n",
      "pii_doc.title:  Learning from healthy and stable eyes: A new approach for detection of glaucomatous progression \n",
      "pii_doc.title:  A methodology based on multiple criteria decision analysis for combining antibiotics in empirical therapy \n",
      "pii_doc.title:  Classifying cancer pathology reports with hierarchical self-attention networks\n",
      "pii_doc.title:  Reinforcement learning application in diabetes blood glucose control: A systematic review \n",
      "pii_doc.title:  A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetes diagnosis \n",
      "pii_doc.title:  Artificial intelligence and the future of psychiatry: Insights from a global physician survey \n",
      "doc_srch has 25 results.\n",
      "pii_doc.title:  A multi-context CNN ensemble for small lesion detection \n",
      "pii_doc.title:  The impact of machine learning on patient care: A systematic review \n",
      "pii_doc.title:  A novel method of motor imagery classification using eeg signal \n",
      "pii_doc.title:  The role of medical smartphone apps in clinical decision-support: A literature review \n",
      "pii_doc.title:  Combining clustering and classification ensembles: A novel pipeline to identify breast cancer profiles \n",
      "pii_doc.title:  Ophthalmic diagnosis using deep learning with fundus images – A critical review \n",
      "pii_doc.title:  A survey on computer-assisted Parkinson's Disease diagnosis \n",
      "pii_doc.title:  Pharmacological therapy selection of type 2 diabetes based on the SWARA and modified MULTIMOORA methods under a fuzzy environment \n",
      "pii_doc.title:  Using echo state networks for classification: A case study in Parkinson's disease diagnosis \n",
      "pii_doc.title:  Prediction of lung cancer incidence on the low-dose computed tomography arm of the National Lung Screening Trial: A dynamic Bayesian network \n",
      "pii_doc.title:  Modeling and processing up-to-dateness of patient information in probabilistic therapy decision support \n",
      "pii_doc.title:  Handling limited datasets with neural networks in medical applications: A small-data approach\n",
      "pii_doc.title:  An effective approach for CT lung segmentation using mask region-based convolutional neural networks \n",
      "pii_doc.title:  Multi-objective evolutionary design of antibiotic treatments \n",
      "pii_doc.title:  Toward Development of PreVoid Alerting System for Nocturnal Enuresis Patients: A Fuzzy-Based Approach for Determining the Level of Liquid Encased in Urinary Bladder \n",
      "pii_doc.title:  A multicenter random forest model for effective prognosis prediction in collaborative clinical research network \n",
      "pii_doc.title:  Predicting dementia with routine care EMR data \n",
      "pii_doc.title:  Fuzzy support vector machine-based personalizing method to address the inter-subject variance problem of physiological signals in a driver monitoring system \n",
      "pii_doc.title:  A recurrent neural network approach to predicting hemoglobin trajectories in patients with End-Stage Renal Disease \n",
      "pii_doc.title:  Learning from healthy and stable eyes: A new approach for detection of glaucomatous progression \n",
      "pii_doc.title:  A methodology based on multiple criteria decision analysis for combining antibiotics in empirical therapy \n",
      "pii_doc.title:  Classifying cancer pathology reports with hierarchical self-attention networks\n",
      "pii_doc.title:  Reinforcement learning application in diabetes blood glucose control: A systematic review \n",
      "pii_doc.title:  A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetes diagnosis \n",
      "pii_doc.title:  Artificial intelligence and the future of psychiatry: Insights from a global physician survey \n",
      "doc_srch has 25 results.\n",
      "pii_doc.title:  A multi-context CNN ensemble for small lesion detection \n",
      "pii_doc.title:  The impact of machine learning on patient care: A systematic review \n",
      "pii_doc.title:  A novel method of motor imagery classification using eeg signal \n",
      "pii_doc.title:  The role of medical smartphone apps in clinical decision-support: A literature review \n",
      "pii_doc.title:  Combining clustering and classification ensembles: A novel pipeline to identify breast cancer profiles \n",
      "pii_doc.title:  Ophthalmic diagnosis using deep learning with fundus images – A critical review \n",
      "pii_doc.title:  A survey on computer-assisted Parkinson's Disease diagnosis \n",
      "pii_doc.title:  Pharmacological therapy selection of type 2 diabetes based on the SWARA and modified MULTIMOORA methods under a fuzzy environment \n",
      "pii_doc.title:  Using echo state networks for classification: A case study in Parkinson's disease diagnosis \n",
      "pii_doc.title:  Prediction of lung cancer incidence on the low-dose computed tomography arm of the National Lung Screening Trial: A dynamic Bayesian network \n",
      "pii_doc.title:  Modeling and processing up-to-dateness of patient information in probabilistic therapy decision support \n",
      "pii_doc.title:  Handling limited datasets with neural networks in medical applications: A small-data approach\n",
      "pii_doc.title:  An effective approach for CT lung segmentation using mask region-based convolutional neural networks \n",
      "pii_doc.title:  Multi-objective evolutionary design of antibiotic treatments \n",
      "pii_doc.title:  Toward Development of PreVoid Alerting System for Nocturnal Enuresis Patients: A Fuzzy-Based Approach for Determining the Level of Liquid Encased in Urinary Bladder \n",
      "pii_doc.title:  A multicenter random forest model for effective prognosis prediction in collaborative clinical research network \n",
      "pii_doc.title:  Predicting dementia with routine care EMR data \n",
      "pii_doc.title:  A recurrent neural network approach to predicting hemoglobin trajectories in patients with End-Stage Renal Disease \n",
      "pii_doc.title:  Fuzzy support vector machine-based personalizing method to address the inter-subject variance problem of physiological signals in a driver monitoring system \n",
      "pii_doc.title:  Learning from healthy and stable eyes: A new approach for detection of glaucomatous progression \n",
      "pii_doc.title:  A methodology based on multiple criteria decision analysis for combining antibiotics in empirical therapy \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pii_doc.title:  Classifying cancer pathology reports with hierarchical self-attention networks\n",
      "pii_doc.title:  Reinforcement learning application in diabetes blood glucose control: A systematic review \n",
      "pii_doc.title:  A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetes diagnosis \n",
      "pii_doc.title:  Artificial intelligence and the future of psychiatry: Insights from a global physician survey \n"
     ]
    }
   ],
   "source": [
    "## Initialize doc search object using ScienceDirect and execute search, \n",
    "#   retrieving all results\n",
    "import pandas as pd\n",
    "n=0\n",
    "while n<=5:\n",
    "    doc_srch = ElsSearch(\"src(artificial intelligence in medicine) AND text(*a*)\",'sciencedirect')\n",
    "    doc_srch.execute(client, get_all = False)\n",
    "    print (\"doc_srch has\", len(doc_srch.results), \"results.\")\n",
    "    results = pd.DataFrame(doc_srch.results)\n",
    "    results.drop_duplicates(subset = \"pii\")\n",
    "    for pii in list(results['pii']):\n",
    "        pii_doc = FullDoc(sd_pii = pii)\n",
    "        if pii_doc.read(client):\n",
    "            print (\"pii_doc.title: \", pii_doc.title)\n",
    "            pii_doc.write()   \n",
    "        else:\n",
    "            print (\"Read document failed.\")\n",
    "    n=n+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 0)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_res.drop_duplicates(subset = \"pii\")\n",
    "for pii in list(all_res['pii']):\n",
    "    pii_doc = FullDoc(sd_pii = pii)\n",
    "    if pii_doc.read(client):\n",
    "        print (\"pii_doc.title: \", pii_doc.title)\n",
    "        pii_doc.write()   \n",
    "    else:\n",
    "        print (\"Read document failed.\")"
   ]
  }
 ],
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